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Impact of rainfall and slopes on soil organic carbon (SOC) dynamics was assessed under tea and coffee plantations, land covers, in Western Ghats Mountain (WGM). Severe water erosion in the western side of WGM by heavy rainfall and land sloppiness recorded less SOC (3.07%) compared to the eastern side (4.03%) in tea plantations. Similar trend in SOC was observed in coffee plantations, with 1.98% in western side and 3.25% in eastern side. Loss of soil organic carbon stock under tea plantations in western sides was 0.346 million tonnes and in coffee plantations it was 0.190 million tonnes. Hence, the total organic carbon loss was 0.536 million tonnes. Highest SOC content and stocks were recorded in steep slopes in tea and coffee plantations due to dense biomass cover and adoption of soil and water conservation measures. [ABSTRACT FROM AUTHOR]

Latent heat released during orographic precipitation over the Western Ghat (WG) Mountain drives the Indian Summer Monsoon. Instrumental data record a considerable spatiotemporal variation of rainfall amount along the mountain. Although Indian teak (Tectona grandis LF) samples from WG have been used earlier for reconstructing past monsoon rainfall, the effect of rainfall variation on the annual ring growth pattern along the mountain has not been extensively studied yet. To address this issue, tree-ring width data series from three locations—Dahanu (1763 -1985), Nagerhole (1703–2016), and Tekkedy (1785–2003) are presented in this study. The locations are situated along WG from north to south. Monthly mean rainfall data obtained from the nearest observatories show a decreasing (increasing) rainfall amount from north to south during summer monsoon (pre and post-monsoon). We obtain a significant positive correlation between ring width indices and monsoon rainfall and a negative correlation with summer (especially May) temperature suggesting a contrasting response of summer temperature and monsoon rain on teak growth. The correlation between rainfall and ring width varies from southern to northern WG (both in significance level and the number of months with significant correlation). While the southernmost location exhibits positive (significant) correlations for both pre and summer monsoon months, the correlations are found for only two summer monsoonal months in the northernmost location. This spatial correlation trend reflects the variation of the pre-monsoon to monsoon rainfall ratio observed in our study locations. This observation is further substantiated by soil moisture-ring width relationship. Furthermore, our study shows that ring width indices respond to a variation of western equatorial Pacific sea surface temperature and vapor pressure deficit. Our study, therefore, suggests that the Indian teak samples can be used for understanding regional and seasonal scale rainfall/soil moisture variation along the WG and teleconnection studies. [ABSTRACT FROM AUTHOR]

The number of large landslides in India has risen in the recent years, due to an increased occurrence of extreme monsoon rainfall events. There is an urgent need to improve our understanding of moisture-induced landslide dynamics, which vary both spatially and temporally. Geophysical methods provide integrated tools to monitor subsurface hydrological processes in unstable slopes at high spatial resolution. They are complementary to more conventional approaches using networks of point sensors, which can provide high temporal resolution information but are severely limited in terms of spatial resolution. Here, we present and discuss data from an electrical resistivity tomography monitoring system—called PRIME—deployed at the Amrita Landslide Early Warning System (Amrita-LEWS) site located in Munnar in the Western Ghats (Kerala, India). The system monitors changes in electrical resistivity in the subsurface of a landslide-prone slope that directly threatens a local community. The monitoring system provides a 4D resistivity model informing on the moisture dynamics in the subsurface of the slope. Results from a 10-month period spanning from pre-monsoon to the end of the monsoon season 2019 are presented and discussed with regard to the spatial variation of soil moisture. The temporal changes in resistivity within the slope are further investigated through the use of time-series clustering and compared to weather and subsurface pore water pressure data. This study sheds new light on the hydrological processes occurring in the shallow subsurface during the monsoon and potentially leading to slope failure. This geophysical approach aims at better understanding and forecasting slope failure to reduce the risk for the local community, thereby providing a powerful tool to be included in local landslide early warning systems. [ABSTRACT FROM AUTHOR]

Analysis of soil samples collected from 16 rice paddy fields located in the Western Ghats region was performed to quantify the concentration of Cu, Zn, Mn, Fe, Ni, Cr, Cd, and Pb using atomic absorption spectroscopy. High concentrations of these heavy metals were found in rice paddy fields regularly cultivated using agrochemicals. We compared this concentration with soils of rice paddy field that was not under cultivation. Cu, Zn, Mn, Fe, Cr, Ni, Pb, and Cd showed increases of 1.2, 1.3, 2.3, 2.2, 1.8, 2.8, 1.8, and 8.5 times, respectively, in the rice paddy fields cultivated with synthetic fertilizers such as NPK, urea, potash, diammonium phosphate, etc., and several categories of pesticides belonging to the class organophosphates, carbamates, and acetanilide herbicide. In contaminated sites, the heavy metals exhibited maximum correlation with soil moisture content (SMC) (Zn, Fe, Cr, Ni, and Cd), soil organic content (SOC) (Fe, Cr, Ni, and Cd), and water holding capacity (WHC) (Cu, Pb, and Cd) than those observed for the reference site. The principal component analysis (PCA) revealed a total of 77.944% variance of heavy metals contributed from WHC (40.259%), SMC (20.854%), and SOC (16.832%). This indicates the build-up of heavy metals in rice paddy soils under the strong influence of moisture content, water holding capacity, and organic carbon content of the soil. [ABSTRACT FROM AUTHOR]

• The warming trend of climate is more than twice in the RS region than in the entire DP. • TWS decreased significantly due to the reduced rate of climate moistening after 2009. • GWSA has the longest response time to rainfall among all constituent components of TWS. • SMSA and GWSA were two major contributors to the TWSA in both the DP and RS regions. • SWSA played a vital role in TWS variability, particularly in the RS region. Inter-state river water disputes are frequent over the rain shadow (RS) region of the Western Ghats (WG), where the annual rainfall is much lower (75.2 cm) than that of overall India (118 cm). This study investigated the TWS (Terrestrial Water Storage) dynamics of the entire Deccan plateau (DP) and RS regions, located at the leeward side of the WG mountain range, with the help of Gravity Recovery and Climate Experiment (GRACE) based TWS anomalies (TWSA) data. Here, the latest TRMM (Tropical Rainfall Measuring Mission) rainfall product was evaluated at different time scales against gauge-based measurements to check its applicability as an alternative to gauge-based observed data. Trends in climate variables and TWSA were estimated to understand their variations during 2003–2016 in both regions. The response of GRACE-based TWSA and its constituent components to rainfall was determined using time-lagged correlation analysis. The contribution of constituent variables to TWSA was estimated using the component contribution ratio (CCR) method. It was observed that the TRMM-3B43 data captured the rainfall pattern over the entire DP with great accuracy. Results showed that the increasing trend of TWS in 2003–2009 had reversed in 2010–2016 due to the reduction of climate moistening in both regions. The warming trend of climate is more than twice in the RS region than that found in the DP, leading to a higher decreasing rate of TWS in this region than in DP. For both DP and RS regions, the time lags of groundwater storage anomalies (GWSA), total runoff anomalies (RA), canopy water storage anomalies (CWSA), and soil moisture storage anomalies (SMSA) to rainfall can be arranged as CWSA ≤ RA ≤ SMSA ≤ GWSA. SMSA and GWSA are the main contributors to the TWSA in both areas. However, SWSA also played a vital role in TWS variability, particularly in the RS region. [ABSTRACT FROM AUTHOR]

Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data were analyzed to study the vegetation dynamics over different meteorological subdivisions in India for the period 2000–2016. Soil moisture (SM), rainfall (RF), and land use land cover (LULC) data were analyzed to identify the climatic and anthropogenic drivers that cause vegetation changes at the subdivision scale. Principal component analysis and MK (Mann-Kendall) test showed significant greening trend over semi-arid regions of Northwest India (NWI) and South India (SI) while slight browning trend seen over some of the subdivisions in Indo-Gangetic (IG) plains and Western Ghats (WG). It is found that the NDVI has superior correlation with soil moisture compared with rainfall and the croplands (CL) found to have significant increasing trend over the NWI and SI. Increasing trend in soil moisture over the NWI and SI may have contributed to increase in CL area and the greening trend. Over IG plains, the NDVI showed moderate correlation with SM and RF, and the greening trend (browning trend) in some regions can be attributed to increase in natural vegetation mosaic (decrease of CL). The NDVI has shown browning trend over the core monsoon regions of Madhya Pradesh (an increase of barren lands over west MP and decrease of CL over east MP) and Western Ghats (significant decrease of CL over Konkan and Goa). This study revealed that the soil moisture and LULC changes are the major driving factors for the vegetation changes over majority of the subdivisions in India. [ABSTRACT FROM AUTHOR]

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The objective of this study is to apply and test a simple parametric water balance model for prediction of soil moisture regime in the presence of vegetation. The intention was to evaluate the differences in model parameterization and performance when applied to small watersheds under three different types of land covers ( Acacia, degraded forest and natural forest). The watersheds selected for this purpose are located in the sub-humid climate within the Western Ghats, Karnataka, India. Model calibration and validation were performed using a dataset comprising depth-averaged soil moisture content measurements made at weekly time steps from October 2004 to December 2008. In addition to this, a sensitivity analysis was carried out with respect to the water-holding capacity of the soils with the aim of explaining the suitability and adaptation of exotic vegetation types under the prevailing climatic conditions. Results indicated reasonably good performance of the model in simulating the pattern and magnitude of weekly average soil moisture content in 150 cm deep soil layer under all three land covers. This study demonstrates that a simple, robust and parametrically parsimonious model is capable of simulating the temporal dynamics of soil moisture content under distinctly different land covers. Also, results of sensitivity analysis revealed that exotic plant species such as Acacia have adapted themselves effectively to the local climate. [ABSTRACT FROM AUTHOR]

Summary: An understanding of the soil moisture variability is necessary to characterize the linkages between a region’s hydrology, ecology and physiography. In the changing land use scenario of Western Ghats, India, where deforestation along with extensive afforestation with exotic species is being undertaken, there is an urgent need to evaluate the impacts of these changes on regional hydrology. The objectives of the present study were: (a) to understand spatio-temporal variability of soil water potential and soil moisture content under different land covers in the humid tropical Western Ghats region and (b) to evaluate differences if any in spatial and temporal patterns of soil moisture content as influenced by nature of land cover. To this end, experimental watersheds located in the Western Ghats of Uttara Kannada District, Karnataka State, India, were established for monitoring of soil moisture. These watersheds possessed homogenous land covers of acacia plantation, natural forest and degraded forest. In addition to the measurements of hydro-meteorological parameters, soil matric potential measurements were made at four locations in each watershed at 50cm, 100cm and 150cm depths at weekly time intervals during the period October 2004–December 2008. Soil moisture contents derived from potential measurements collected were analyzed to characterize the spatial and temporal variations across the three land covers. The results of ANOVA (p <0.01, LSD) test indicated that there was no significant change in the mean soil moisture across land covers. However, significant differences in soil moisture with depth were observed under forested watershed, whereas no such changes with depth were noticed under acacia and degraded land covers. Also, relationships between soil moisture at different depths were evaluated using correlation analysis and multiple linear regression models for prediction of soil moisture from climatic variables and antecedent moisture condition were developed and tested. A regression model relating near-surface soil moisture (50cm) with profile soil moisture content was developed which may prove useful when surface soil moisture contents derived from satellite remote sensing are available. Overall results of this study indicate that while the nature of land cover has an influence on the spatio-temporal variability of soil moisture, other variables related to topography may have a more dominant effect. [Copyright &y& Elsevier]

Precipitation obtained from rain gauges is an essential input for hydrological modelling. It is often sparse in highly topographically varying terrain, exhibiting a certain amount of uncertainty in hydrological modelling. Hence, satellite rainfall estimates have been used as an alternative or as a supplement to station observations. In this study, an attempt was made to evaluate the Tropical Rainfall Measuring Mission (TRMM) and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), employing a semi-distributed hydrological model, i.e., Soil and Water Assessment Tool (SWAT), for simulating streamflow and validating them against the flows generated by the India Meteorological Department (IMD) rainfall dataset in the Gurupura river catchment of India. Distinct testing scenarios for simulating streamflow were made to check the suitability of these satellite precipitation data. The TRMM was able to better estimate rainfall than CHIRPS after performing categorical and continuous statistical results with respect to IMD rainfall data. While comparing the performance of model simulations, the IMD rainfall-driven streamflow emerged as the best followed by the TRMM, CHIRPS-0.05, and CHIRPS-0.25. The coefficient of determination (R2), Nash–Sutcliffe efficiency (NSE), and percent bias (PBIAS) were in the range 0.63 to 0.86, 0.62 to 0.86, and −14.98 to 0.87, respectively. Further, an attempt was made to examine the spatial distribution of key hydrological signature, i.e., flow duration curve (FDC) in the 30–95 percentile range of non-exceedance probability. It was observed that TRMM underestimated the flow for agricultural water availability corresponding to 30 percent, even though it showed a good performance compared to the other satellite rainfall-driven model outputs. [ABSTRACT FROM AUTHOR]

It is imperative to know the spatial distribution of vegetation trends in India and its responses to both climatic and non-climatic drivers because many ecoregions are vulnerable to global climate change. Here we employed the NDVI3g satellite data over the span of 35 years (1981/82–2015) to estimate vegetation trends and corresponding climatic variables trends (i.e., precipitation, temperature, solar radiation and soil moisture) by using the Mann–Kendall test (τ) and the Theil–Sen median trend. Analysis was performed separately for the two focal periods—(i) the earlier period (1981/82–2000) and (ii) later period (2000–2015)—because many ecoregions experienced more warming after 2000 than the 1980s and 1990s. Our results revealed that a prominent large-scale greening trend (47% of area) of vegetation continued from the earlier period to the later period (80% of area) across the northwestern Plain and Central India. Despite climatologically drier regions, the stronger greening trend was also evident over croplands which was attributed to moisture-induced greening combined with cooling trends of temperature. However, greening trends of vegetation and croplands diminished (i.e., from 84% to 40% of area in kharif season), especially over the southern peninsula, including the west-central area. Such changes were mostly attributed to warming trends and declined soil moisture trends, a phenomenon known as temperature-induced moisture stress. This effect has an adverse impact on vegetation growth in the Himalayas, Northeast India, the Western Ghats and the southern peninsula, which was further exaggerated by human-induced land-use change. Therefore, it can be concluded that vegetation trend analysis from NDVI3g data provides vital information on two mechanisms (i.e., temperature-induced moisture stress and moisture-induced greening) operating in India. In particular, the temperature-induced moisture stress is alarming, and may be exacerbated in the future under accelerated warming as it may have potential implications on forest and agriculture ecosystems, including societal impacts (e.g., food security, employment, wealth). These findings are very valuable to policymakers and climate change awareness-raising campaigns at the national level. [ABSTRACT FROM AUTHOR]